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The "ORION" Spinal System is a non-cervical, pedicle screw system intended to provide posterior, non-cervical immobilization and stabilization of spinal segments in skeletally mature patients as an adjunct to fusion in the treatment of the following acute and chronic instabilities or deformities of the thoracic, lumbar, and sacral spine : degenerative spondylolisthesis with objective evidence of neurological impairment; fracture; scoliosis; kyphosis; spinal stenosis; tumor; pseudarthrosis and/or failed previous fusion.

In addition, the "ORION" Spinal System is also indicated for the treatment of the skeletally mature patients with severe spondylolisthesis (Grade 3 and 4) of the L5-S1 vertebra, having fusion by autogenous bone graft, with implant fixed or attached to the lumbar and sacral spine (L3 to Sacrum), and for whom the intended to be removed after solid fusion is achieved.

The "ORION" Spinal System is a system that is intended to be used for posterior thoracolumbar fusion procedures. The system is manufactured from Ti-6Al-4V which complies with ASTM F136 and PEEK which complies with ASTM F2026. The components, which are included as part of the system, include screws, rods, links, and accessory connection components.

This is a 510(k) premarket notification for the "ORION" Spinal System. This document does not describe the acceptance criteria and study for an AI/ML device. It's for a traditional medical device (a pedicle screw system). Therefore, the requested information regarding AI/ML device testing criteria, such as sample size for test/training sets, data provenance, expert ground truth, adjudication methods, MRMC studies, or standalone algorithm performance, is not applicable and not present in this document.

The document discusses the substantial equivalence of the "ORION" Spinal System to a predicate device based on its indications for use, technological characteristics, and performance data from engineering tests.

Here's what information is provided:

1. A table of acceptance criteria and the reported device performance:

This document does not provide a table comparing acceptance criteria and reported device performance in the way typically expected for AI/ML performance metrics (e.g., sensitivity, specificity, AUC). Instead, it lists the types of engineering tests performed on the "ORION" Spinal System:

The document states that the "subect device is substantially equivalent to the predicate device," implying that the performance in these tests met the established criteria for substantial equivalence to the predicate device and relevant ASTM standards. However, the specific numerical results or acceptance thresholds from these tests are not detailed in this summary.

2. Sample sized used for the test set and the data provenance: Not applicable. This is an engineering test of a physical device, not an AI/ML algorithm evaluated on a data set.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Not applicable. This is an engineering test of a physical device.

4. Adjudication method (e.g. 2+1, 3+1, none) for the test set: Not applicable. This is an engineering test of a physical device.

5. If a multi reader multi case (MRMC) comparative effectiveness study was done, If so, what was the effect size of how much human readers improve with AI vs without AI assistance: Not applicable. This is an engineering test of a physical device.

6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done: Not applicable. This is an engineering test of a physical device.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.): Not applicable to an AI context. For this device, the "ground truth" implicitly refers to the device's ability to withstand mechanical forces and maintain structural integrity as per relevant ASTM standards, demonstrating substantial equivalence to the predicate device.

8. The sample size for the training set: Not applicable. This is an engineering test of a physical device, not an AI/ML algorithm.

9. How the ground truth for the training set was established: Not applicable. This is an engineering test of a physical device, not an AI/ML algorithm.

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